This study aims to demonstrate the utility and feasibility of decomposing vocal tract electromyographic signals to their constituent motor unit action potentials. This will provide a picture of the myoelectric activities of the many motor units in a local area of the muscle in their appropriate magnitude and temporal relations. This has not been previously accomplished for vocal tract musculature and has only recently become viable because specialized recording needle electrodes and amplifiers have become commercially available. The availability of these constituent signals will permit the observation of underlying neural pathology during the manifestation of disorders affecting voice and speech, thus greatly enhancing our understanding of neuromotor disease physiology. The thyroarytenoid and cricothyroid muscles of spasmodic dysphonia subjects will be used as models for the study. An emerging class of powerful signal processing technologies, such as time-frequency wavelet transforms and singular value decomposition, will be applied to the composite electromyographic waveforms to provide new representations of the dynamic frequency content of the signal. Since the nervous system encodes its information to the muscle in terms of frequency, these transforms may reveal pattern signatures of disordered pathology that may be embedded in the myoelectric signals. These technologies have been successfully applied in modern applications of space technology, "smart weapons", and radar arrays where signals measured at the periphery are described as containing multivariate, poor-context frequency information in the presence of noise. Although signals and objectives are highly analogous, transfer of these modern processing technologies to laryn-gologic research has been greatly lacking. The constituent neural signals obtained by decomposition together with the new frequency representations provide the framework for identifying pattern signatures of the controlling system using peripheral measurements. Once pattern signatures of neuromuscular pathologies become identified in the myoelectric signals of laryngeal muscles, processes that identify and correlate pattern signatures obtained from ore remote (and less invasive) recording fields, such as surface array microdetectors (e.g. electrodes, accelerometers, magnetometers, ultra-sound), makes it possible to achieve equal success as in analogous applications. This ability, in turn, will have a profound impact on early detection of diseases that manifest with vocal symptoms, such as Parkinson's and ALS, and for the pre-surgical testing of the propensity for potentially life- threatening laryngeal tonic closure (spasm) due to intubation trauma.